This paper presents a dynamic tree-based item response (IRTree) model as a novel extension of the autoregressive generalized linear mixed effect model (dynamic GLMM). We illustrate the unique utility of the dynamic IRTree model in its capability of modeling differentiated processes indicated by intensive polytomous time-series eye-tracking data. The dynamic IRTree was inspired by but is distinct from the dynamic GLMM which was previously presented by Cho, Brown-Schmidt, and Lee (Psychometrika 83(3):751–771, 2018). Unlike the dynamic IRTree, the dynamic GLMM is suitable for modeling intensive binary time-series eye-tracking data to identify visual attention to a single interest area over all other possible fixation locations. The dynamic IRTree model is a general modeling framework which can be used to model change processes (trend and autocorrelation) and which allows for decomposing data into various sources of heterogeneity. The dynamic IRTree model was illustrated using an experimental study that employed the visual-world eye-tracking technique. The results of a simulation study showed that parameter recovery of the model was satisfactory and that ignoring trend and autoregressive effects resulted in biased estimates of experimental condition effects in the same conditions found in the empirical study.

中文翻译：

---

建模密集的多分时间序列眼动追踪数据：基于动态树的项目响应模型

本文提出了一种基于动态树的项目响应 (IRTree) 模型，作为自回归广义线性混合效应模型 (dynamic GLMM) 的新扩展。我们说明了动态 IRTree 模型在对由密集的多分时间序列眼动追踪数据指示的差异化过程进行建模的能力方面的独特效用。动态 IRTree 的灵感来自于之前由 Cho、Brown-Schmidt 和 Lee 提出的动态 GLMM（Psychometrika 83(3):751–771, 2018），但与之不同。与动态 IRTree 不同，动态 GLMM 适用于对密集的二进制时间序列眼动追踪数据进行建模，以识别对所有其他可能注视位置上的单个兴趣区域的视觉注意力。动态 IRTree 模型是一个通用建模框架，可用于对变化过程（趋势和自相关）进行建模，并允许将数据分解为各种异质性来源。使用视觉世界眼动追踪技术的实验研究说明了动态 IRTree 模型。模拟研究的结果表明，模型的参数恢复是令人满意的，忽略趋势和自回归效应会导致在实证研究中发现的相同条件下对实验条件效应的估计有偏差。